{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "695cebbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "!pip install -Uqq nixtla datasetsforecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "579b1471",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "from nixtla.utils import in_colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93693704",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "IN_COLAB = in_colab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ff4759",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    from nixtla.utils import colab_badge\n",
    "    from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24c899f2-78c1-43b2-8347-3164e3549c3f",
   "metadata": {},
   "source": [
    "# Categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a81fc39a-c6a0-485d-a3f3-c3a6298928a6",
   "metadata": {},
   "source": [
    "Categorical variables are external factors that can influence a forecast. These variables take on one of a limited, fixed number of possible values, and induce a grouping of your observations.\\\n",
    "\\\n",
    "For example, if you're forecasting daily product demand for a retailer, you could benefit from an event variable that may tell you what kind of event takes place on a given day, for example 'None', 'Sporting', or 'Cultural'.\\\n",
    "\\\n",
    "To incorporate categorical variables in TimeGPT, you'll need to pair each point in your time series data with the corresponding external data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bc1e229-6c80-463f-b721-465a48fbddf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/03_categorical_variables.ipynb)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| echo: false\n",
    "if not IN_COLAB:\n",
    "    load_dotenv()    \n",
    "    colab_badge('docs/tutorials/03_categorical_variables')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "957acb84",
   "metadata": {},
   "source": [
    "## 1. Import packages\n",
    "First, we install and import the required packages and initialize the Nixtla client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a84a0f65-e084-4e65-a0fb-d27c184dde44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "from nixtla import NixtlaClient\n",
    "from datasetsforecast.m5 import M5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "469d474a-c427-427c-a127-d140aeba0354",
   "metadata": {},
   "outputs": [],
   "source": [
    "nixtla_client = NixtlaClient(\n",
    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
    "    api_key = 'my_api_key_provided_by_nixtla'   \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38badfe",
   "metadata": {},
   "source": [
    "> 👍 Use an Azure AI endpoint\n",
    "> \n",
    "> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
    "> \n",
    "> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ced2bba4-089d-4733-9613-33a16cad622b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    nixtla_client = NixtlaClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6f16ac",
   "metadata": {},
   "source": [
    "## 2. Load M5 data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc2bb3db-00e6-44e6-8dc3-a2e0eba7e295",
   "metadata": {},
   "source": [
    "Let's see an example on predicting sales of products of the [M5 dataset](https://nixtlaverse.nixtla.io/datasetsforecast/m5.html). The M5 dataset contains daily product demand (sales) for 10 retail stores in the US.\\\n",
    "\\\n",
    "First, we load the data using `datasetsforecast`. This returns:\n",
    "\n",
    "* `Y_df`, containing the sales (`y` column), for each unique product (`unique_id` column) at every timestamp (`ds` column). \n",
    "* `X_df`, containing additional relevant information for each unique product (`unique_id` column) at every timestamp (`ds` column). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fec19dc-48dd-4337-8678-fe3753b5eb30",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50.2M/50.2M [00:04<00:00, 12.2MiB/s]  \n",
      "INFO:datasetsforecast.utils:Successfully downloaded m5.zip, 50219189, bytes.\n",
      "INFO:datasetsforecast.utils:Decompressing zip file...\n",
      "INFO:datasetsforecast.utils:Successfully decompressed c:\\Users\\ospra\\OneDrive\\Phd\\Repositories\\nixtla\\m5\\datasets\\m5.zip\n",
      "c:\\Users\\ospra\\miniconda3\\envs\\nixtla\\lib\\site-packages\\datasetsforecast\\m5.py:143: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  keep_mask = long.groupby('id')['y'].transform(first_nz_mask, engine='numba')\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-02</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-03</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-05</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          unique_id         ds    y\n",
       "0  FOODS_1_001_CA_1 2011-01-29  3.0\n",
       "1  FOODS_1_001_CA_1 2011-01-30  0.0\n",
       "2  FOODS_1_001_CA_1 2011-01-31  0.0\n",
       "3  FOODS_1_001_CA_1 2011-02-01  1.0\n",
       "4  FOODS_1_001_CA_1 2011-02-02  4.0\n",
       "5  FOODS_1_001_CA_1 2011-02-03  2.0\n",
       "6  FOODS_1_001_CA_1 2011-02-04  0.0\n",
       "7  FOODS_1_001_CA_1 2011-02-05  2.0\n",
       "8  FOODS_1_001_CA_1 2011-02-06  0.0\n",
       "9  FOODS_1_001_CA_1 2011-02-07  0.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_df, X_df, _ = M5.load(directory=os.getcwd())\n",
    "Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "X_df['ds'] = pd.to_datetime(X_df['ds'])\n",
    "Y_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ed2196b",
   "metadata": {},
   "source": [
    "For this example, we will only keep the additional relevant information from the column `event_type_1`. This column is a _categorical variable_ that indicates whether an important event that might affect the sales of the product takes place at a certain date. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105dfb00",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>event_type_1</th>\n",
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       "      <th>0</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-01-29</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>nan</td>\n",
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       "      <th>3</th>\n",
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       "      <td>nan</td>\n",
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       "      <th>4</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-02</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-03</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-04</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-05</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-06</td>\n",
       "      <td>Sporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FOODS_1_001_CA_1</td>\n",
       "      <td>2011-02-07</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          unique_id         ds event_type_1\n",
       "0  FOODS_1_001_CA_1 2011-01-29          nan\n",
       "1  FOODS_1_001_CA_1 2011-01-30          nan\n",
       "2  FOODS_1_001_CA_1 2011-01-31          nan\n",
       "3  FOODS_1_001_CA_1 2011-02-01          nan\n",
       "4  FOODS_1_001_CA_1 2011-02-02          nan\n",
       "5  FOODS_1_001_CA_1 2011-02-03          nan\n",
       "6  FOODS_1_001_CA_1 2011-02-04          nan\n",
       "7  FOODS_1_001_CA_1 2011-02-05          nan\n",
       "8  FOODS_1_001_CA_1 2011-02-06     Sporting\n",
       "9  FOODS_1_001_CA_1 2011-02-07          nan"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_df = X_df[['unique_id', 'ds', 'event_type_1']]\n",
    "\n",
    "X_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbc97b8",
   "metadata": {},
   "source": [
    "As you can see, on February 6th 2011, there is a Sporting event. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b360eb3d",
   "metadata": {},
   "source": [
    "## 3. Forecasting product demand using categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2d60ea6",
   "metadata": {},
   "source": [
    "We will forecast the demand for a single product only. We choose a high selling food product identified by `FOODS_3_090_CA_3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29fe0c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "product = 'FOODS_3_090_CA_3'\n",
    "Y_df_product = Y_df.query('unique_id == @product')\n",
    "X_df_product = X_df.query('unique_id == @product')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f5a78f",
   "metadata": {},
   "source": [
    "We merge our two dataframes to create the dataset to be used in TimeGPT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c4d8b75",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2011-01-31</td>\n",
       "      <td>102.0</td>\n",
       "      <td>nan</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2011-02-02</td>\n",
       "      <td>106.0</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2011-02-03</td>\n",
       "      <td>123.0</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2011-02-04</td>\n",
       "      <td>279.0</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2011-02-05</td>\n",
       "      <td>175.0</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2011-02-06</td>\n",
       "      <td>186.0</td>\n",
       "      <td>Sporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2011-02-07</td>\n",
       "      <td>120.0</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          unique_id         ds      y event_type_1\n",
       "0  FOODS_3_090_CA_3 2011-01-29  108.0          nan\n",
       "1  FOODS_3_090_CA_3 2011-01-30  132.0          nan\n",
       "2  FOODS_3_090_CA_3 2011-01-31  102.0          nan\n",
       "3  FOODS_3_090_CA_3 2011-02-01  120.0          nan\n",
       "4  FOODS_3_090_CA_3 2011-02-02  106.0          nan\n",
       "5  FOODS_3_090_CA_3 2011-02-03  123.0          nan\n",
       "6  FOODS_3_090_CA_3 2011-02-04  279.0          nan\n",
       "7  FOODS_3_090_CA_3 2011-02-05  175.0          nan\n",
       "8  FOODS_3_090_CA_3 2011-02-06  186.0     Sporting\n",
       "9  FOODS_3_090_CA_3 2011-02-07  120.0          nan"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = Y_df_product.merge(X_df_product)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47873237",
   "metadata": {},
   "source": [
    "In order to use _categorical variables_ with TimeGPT, it is necessary to numerically encode the variables. We will use _one-hot encoding_ in this tutorial. \n",
    "\n",
    "We can one-hot encode the `event_type_1` column by using pandas built-in `get_dummies` functionality. After one-hot encoding the `event_type_1` variable, we can add it to the dataframe and remove the original column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee09d3c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>Cultural</th>\n",
       "      <th>National</th>\n",
       "      <th>Religious</th>\n",
       "      <th>Sporting</th>\n",
       "      <th>nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-10</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-11</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-12</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-14</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-15</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-16</td>\n",
       "      <td>116.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-17</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-18</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-06-19</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             unique_id         ds      y  Cultural  National  Religious  \\\n",
       "1959  FOODS_3_090_CA_3 2016-06-10  140.0         0         0          0   \n",
       "1960  FOODS_3_090_CA_3 2016-06-11  151.0         0         0          0   \n",
       "1961  FOODS_3_090_CA_3 2016-06-12   87.0         0         0          0   \n",
       "1962  FOODS_3_090_CA_3 2016-06-13   67.0         0         0          0   \n",
       "1963  FOODS_3_090_CA_3 2016-06-14   50.0         0         0          0   \n",
       "1964  FOODS_3_090_CA_3 2016-06-15   58.0         0         0          0   \n",
       "1965  FOODS_3_090_CA_3 2016-06-16  116.0         0         0          0   \n",
       "1966  FOODS_3_090_CA_3 2016-06-17  124.0         0         0          0   \n",
       "1967  FOODS_3_090_CA_3 2016-06-18  167.0         0         0          0   \n",
       "1968  FOODS_3_090_CA_3 2016-06-19  118.0         0         0          0   \n",
       "\n",
       "      Sporting  nan  \n",
       "1959         0    1  \n",
       "1960         0    1  \n",
       "1961         0    1  \n",
       "1962         0    1  \n",
       "1963         0    1  \n",
       "1964         0    1  \n",
       "1965         0    1  \n",
       "1966         0    1  \n",
       "1967         0    1  \n",
       "1968         1    0  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_type_1_ohe = pd.get_dummies(df['event_type_1'], dtype=int)\n",
    "df = pd.concat([df, event_type_1_ohe], axis=1)\n",
    "df = df.drop(columns = 'event_type_1')\n",
    "\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c86f2a4",
   "metadata": {},
   "source": [
    "As you can see, we have now added 5 columns, each with a binary indicator (`1` or `0`) whether there is a `Cultural`, `National`, `Religious`, `Sporting` or no (`nan`) event on that particular day. For example, on June 19th 2016, there is a `Sporting` event."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ef0388c",
   "metadata": {},
   "source": [
    "Let's turn to our forecasting task. We will forecast the first 7 days of February 2016. This includes 7 February 2016 - the date on which [Super Bowl 50](https://en.wikipedia.org/wiki/Super_Bowl_50) was held. Such large, national events typically impact retail product sales.\n",
    "\n",
    "To use the encoded categorical variables in TimeGPT, we have to add them as future values. Therefore, we create a future values dataframe, that contains the `unique_id`, the timestamp `ds`, and the encoded categorical variables.\n",
    "\n",
    "Of course, we drop the target column as this is normally not available - this is the quantity that we seek to forecast!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c17c641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>Cultural</th>\n",
       "      <th>National</th>\n",
       "      <th>Religious</th>\n",
       "      <th>Sporting</th>\n",
       "      <th>nan</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1833</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             unique_id         ds  Cultural  National  Religious  Sporting  \\\n",
       "1829  FOODS_3_090_CA_3 2016-02-01         0         0          0         0   \n",
       "1830  FOODS_3_090_CA_3 2016-02-02         0         0          0         0   \n",
       "1831  FOODS_3_090_CA_3 2016-02-03         0         0          0         0   \n",
       "1832  FOODS_3_090_CA_3 2016-02-04         0         0          0         0   \n",
       "1833  FOODS_3_090_CA_3 2016-02-05         0         0          0         0   \n",
       "1834  FOODS_3_090_CA_3 2016-02-06         0         0          0         0   \n",
       "1835  FOODS_3_090_CA_3 2016-02-07         0         0          0         1   \n",
       "\n",
       "      nan  \n",
       "1829    1  \n",
       "1830    1  \n",
       "1831    1  \n",
       "1832    1  \n",
       "1833    1  \n",
       "1834    1  \n",
       "1835    0  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_ex_vars_df = df.drop(columns = ['y'])\n",
    "future_ex_vars_df = future_ex_vars_df.query(\"ds >= '2016-02-01' & ds <= '2016-02-07'\")\n",
    "\n",
    "future_ex_vars_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09663187",
   "metadata": {},
   "source": [
    "Next, we limit our input dataframe to all but the 7 forecast days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9955cc75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>Cultural</th>\n",
       "      <th>National</th>\n",
       "      <th>Religious</th>\n",
       "      <th>Sporting</th>\n",
       "      <th>nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1819</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-22</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1820</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-23</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>146.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1822</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1823</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-26</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-27</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-28</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-29</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1827</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-30</td>\n",
       "      <td>113.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             unique_id         ds      y  Cultural  National  Religious  \\\n",
       "1819  FOODS_3_090_CA_3 2016-01-22   94.0         0         0          0   \n",
       "1820  FOODS_3_090_CA_3 2016-01-23  144.0         0         0          0   \n",
       "1821  FOODS_3_090_CA_3 2016-01-24  146.0         0         0          0   \n",
       "1822  FOODS_3_090_CA_3 2016-01-25   87.0         0         0          0   \n",
       "1823  FOODS_3_090_CA_3 2016-01-26   73.0         0         0          0   \n",
       "1824  FOODS_3_090_CA_3 2016-01-27   62.0         0         0          0   \n",
       "1825  FOODS_3_090_CA_3 2016-01-28   64.0         0         0          0   \n",
       "1826  FOODS_3_090_CA_3 2016-01-29  102.0         0         0          0   \n",
       "1827  FOODS_3_090_CA_3 2016-01-30  113.0         0         0          0   \n",
       "1828  FOODS_3_090_CA_3 2016-01-31   98.0         0         0          0   \n",
       "\n",
       "      Sporting  nan  \n",
       "1819         0    1  \n",
       "1820         0    1  \n",
       "1821         0    1  \n",
       "1822         0    1  \n",
       "1823         0    1  \n",
       "1824         0    1  \n",
       "1825         0    1  \n",
       "1826         0    1  \n",
       "1827         0    1  \n",
       "1828         0    1  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df.query(\"ds < '2016-02-01'\")\n",
    "\n",
    "df_train.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f1e41d-e5bf-4d01-aa68-1a7a7fbb579b",
   "metadata": {},
   "source": [
    "Let's call the `forecast` method, first _without_ the categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d252a0e0-f393-4957-8173-230972fc7a40",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: D\n",
      "INFO:nixtla.nixtla_client:Restricting input...\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>TimeGPT</th>\n",
       "      <th>TimeGPT-lo-90</th>\n",
       "      <th>TimeGPT-lo-80</th>\n",
       "      <th>TimeGPT-hi-80</th>\n",
       "      <th>TimeGPT-hi-90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>73.304092</td>\n",
       "      <td>53.449049</td>\n",
       "      <td>54.795078</td>\n",
       "      <td>91.813107</td>\n",
       "      <td>93.159136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-02</td>\n",
       "      <td>66.335518</td>\n",
       "      <td>47.510669</td>\n",
       "      <td>50.274136</td>\n",
       "      <td>82.396899</td>\n",
       "      <td>85.160367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-03</td>\n",
       "      <td>65.881630</td>\n",
       "      <td>36.218617</td>\n",
       "      <td>41.388896</td>\n",
       "      <td>90.374364</td>\n",
       "      <td>95.544643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-04</td>\n",
       "      <td>72.371864</td>\n",
       "      <td>-26.683115</td>\n",
       "      <td>25.097362</td>\n",
       "      <td>119.646367</td>\n",
       "      <td>171.426844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>95.141045</td>\n",
       "      <td>-2.084882</td>\n",
       "      <td>34.027078</td>\n",
       "      <td>156.255011</td>\n",
       "      <td>192.366971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          unique_id          ds    TimeGPT  TimeGPT-lo-90  TimeGPT-lo-80  \\\n",
       "0  FOODS_3_090_CA_3  2016-02-01  73.304092      53.449049      54.795078   \n",
       "1  FOODS_3_090_CA_3  2016-02-02  66.335518      47.510669      50.274136   \n",
       "2  FOODS_3_090_CA_3  2016-02-03  65.881630      36.218617      41.388896   \n",
       "3  FOODS_3_090_CA_3  2016-02-04  72.371864     -26.683115      25.097362   \n",
       "4  FOODS_3_090_CA_3  2016-02-05  95.141045      -2.084882      34.027078   \n",
       "\n",
       "   TimeGPT-hi-80  TimeGPT-hi-90  \n",
       "0      91.813107      93.159136  \n",
       "1      82.396899      85.160367  \n",
       "2      90.374364      95.544643  \n",
       "3     119.646367     171.426844  \n",
       "4     156.255011     192.366971  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timegpt_fcst_without_cat_vars_df = nixtla_client.forecast(df=df_train, h=7, level=[80, 90])\n",
    "timegpt_fcst_without_cat_vars_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d2d153",
   "metadata": {},
   "source": [
    "> 📘 Available models in Azure AI\n",
    ">\n",
    "> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
    ">\n",
    "> `nixtla_client.forecast(..., model=\"azureai\")`\n",
    "> \n",
    "> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
    "> \n",
    "> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c33a6a5",
   "metadata": {},
   "source": [
    "We plot the forecast and the last 28 days before the forecast period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18f36e5c-f41f-4888-b279-97558b71c1bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df[['unique_id', 'ds', 'y']].query(\"ds <= '2016-02-07'\"), \n",
    "    timegpt_fcst_without_cat_vars_df, \n",
    "    max_insample_length=28, \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6028d3f",
   "metadata": {},
   "source": [
    "TimeGPT already provides a reasonable forecast, but it seems to somewhat underforecast the peak on the 6th of February 2016 - the day before the Super Bowl."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bede86e",
   "metadata": {},
   "source": [
    "Let's call the `forecast` method again, now _with_ the categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "928502f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: D\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: Cultural, National, Religious, Sporting, nan\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>TimeGPT</th>\n",
       "      <th>TimeGPT-lo-90</th>\n",
       "      <th>TimeGPT-lo-80</th>\n",
       "      <th>TimeGPT-hi-80</th>\n",
       "      <th>TimeGPT-hi-90</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>70.661271</td>\n",
       "      <td>-0.204378</td>\n",
       "      <td>14.593348</td>\n",
       "      <td>126.729194</td>\n",
       "      <td>141.526919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-02</td>\n",
       "      <td>65.566941</td>\n",
       "      <td>-20.394326</td>\n",
       "      <td>11.654239</td>\n",
       "      <td>119.479643</td>\n",
       "      <td>151.528208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-03</td>\n",
       "      <td>68.510010</td>\n",
       "      <td>-33.713710</td>\n",
       "      <td>6.732952</td>\n",
       "      <td>130.287069</td>\n",
       "      <td>170.733731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-04</td>\n",
       "      <td>75.417710</td>\n",
       "      <td>-40.974649</td>\n",
       "      <td>4.751767</td>\n",
       "      <td>146.083653</td>\n",
       "      <td>191.810069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>2016-02-05</td>\n",
       "      <td>97.340302</td>\n",
       "      <td>-57.385361</td>\n",
       "      <td>18.253812</td>\n",
       "      <td>176.426792</td>\n",
       "      <td>252.065965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          unique_id          ds    TimeGPT  TimeGPT-lo-90  TimeGPT-lo-80  \\\n",
       "0  FOODS_3_090_CA_3  2016-02-01  70.661271      -0.204378      14.593348   \n",
       "1  FOODS_3_090_CA_3  2016-02-02  65.566941     -20.394326      11.654239   \n",
       "2  FOODS_3_090_CA_3  2016-02-03  68.510010     -33.713710       6.732952   \n",
       "3  FOODS_3_090_CA_3  2016-02-04  75.417710     -40.974649       4.751767   \n",
       "4  FOODS_3_090_CA_3  2016-02-05  97.340302     -57.385361      18.253812   \n",
       "\n",
       "   TimeGPT-hi-80  TimeGPT-hi-90  \n",
       "0     126.729194     141.526919  \n",
       "1     119.479643     151.528208  \n",
       "2     130.287069     170.733731  \n",
       "3     146.083653     191.810069  \n",
       "4     176.426792     252.065965  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timegpt_fcst_with_cat_vars_df = nixtla_client.forecast(df=df_train, X_df=future_ex_vars_df, h=7, level=[80, 90])\n",
    "timegpt_fcst_with_cat_vars_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67812465",
   "metadata": {},
   "source": [
    "> 📘 Available models in Azure AI\n",
    ">\n",
    "> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
    ">\n",
    "> `nixtla_client.forecast(..., model=\"azureai\")`\n",
    "> \n",
    "> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
    "> \n",
    "> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82686c05",
   "metadata": {},
   "source": [
    "We plot the forecast and the last 28 days before the forecast period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fd659c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df[['unique_id', 'ds', 'y']].query(\"ds <= '2016-02-07'\"), \n",
    "    timegpt_fcst_with_cat_vars_df, \n",
    "    max_insample_length=28, \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb892aba",
   "metadata": {},
   "source": [
    "We can visually verify that the forecast is closer to the actual observed value, which is the result of including the categorical variable in our forecast."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93499305",
   "metadata": {},
   "source": [
    "Let's verify this conclusion by computing the [Mean Absolute Error](https://en.wikipedia.org/wiki/Mean_absolute_error) on the forecasts we created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65cdaab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utilsforecast.losses import mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de1fd15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create target dataframe\n",
    "df_target = df[['unique_id', 'ds', 'y']].query(\"ds >= '2016-02-01' & ds <= '2016-02-07'\")\n",
    "\n",
    "# Rename forecast columns\n",
    "timegpt_fcst_without_cat_vars_df = timegpt_fcst_without_cat_vars_df.rename(columns={'TimeGPT': 'TimeGPT-without-cat-vars'})\n",
    "timegpt_fcst_with_cat_vars_df = timegpt_fcst_with_cat_vars_df.rename(columns={'TimeGPT': 'TimeGPT-with-cat-vars'})\n",
    "\n",
    "# Merge forecasts with target dataframe\n",
    "df_target = df_target.merge(timegpt_fcst_without_cat_vars_df[['unique_id', 'ds', 'TimeGPT-without-cat-vars']])\n",
    "df_target = df_target.merge(timegpt_fcst_with_cat_vars_df[['unique_id', 'ds', 'TimeGPT-with-cat-vars']])\n",
    "\n",
    "# Compute errors\n",
    "mean_absolute_errors = mae(df_target, ['TimeGPT-without-cat-vars', 'TimeGPT-with-cat-vars'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8c87654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>TimeGPT-without-cat-vars</th>\n",
       "      <th>TimeGPT-with-cat-vars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FOODS_3_090_CA_3</td>\n",
       "      <td>24.285649</td>\n",
       "      <td>20.028514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          unique_id  TimeGPT-without-cat-vars  TimeGPT-with-cat-vars\n",
       "0  FOODS_3_090_CA_3                 24.285649              20.028514"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_errors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5938cd01",
   "metadata": {},
   "source": [
    "Indeed, we find that the error when using TimeGPT with the categorical variable is approx. 20% lower than when using TimeGPT without the categorical variables, indicating better performance when we include the categorical variable."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
